Privacy Safe Synthetic Data for Healthcare and Finance AI

Real costs of using real data in regulated industries

Real-world data projects in healthcare and finance often run into three bottlenecks. First, data access is slow. A hospital may need to submit paperwork, get ethics approval, and wait weeks before a data access committee reviews a request. Second, data labeling is expensive. Clinical notes, X‑ray images, or transaction logs require expert annotators who charge high hourly rates. Third, legal teams must audit every dataset to ensure compliance with GDPR, HIPAA, or financial regulations. A typical enterprise report shows that data access and preparation can consume 60-80% of a project timeline, leaving less time for actual model development.

What synthetic data actually does

Synthetic data generation starts with a statistical model of real observations. Machine learning algorithms learn the underlying patterns from real records and then produce new examples that follow the same distribution. In healthcare, synthetic EHR data can preserve age distributions, comorbidities, and lab values while scrubbing names, dates of birth, and addresses. In finance, synthetic transaction logs can mimic card usage patterns, merchant categories, and timing without exposing customer identities. The key is that synthetic samples are indistinguishable to the AI model from real data, but they are completely fabricated. This means you can share synthetic datasets across teams, partners, or even public repositories without legal risk.

How synthetic data is created and validated

Creating high-quality synthetic data involves several steps. First, you gather a representative real dataset and clean it. Then you train a generative model, often a GAN, VAE, or more recent diffusion-based methods, to capture the joint distribution of features. After generation, you run statistical tests to compare the synthetic distribution against the real one using metrics like KL divergence, Wasserstein distance, and coverage of rare events. You also run a classifier to check whether a human or model can reliably distinguish synthetic from real examples. If the classifier accuracy is near chance, the synthetic data is considered statistically faithful. In practice, teams report that synthetic data can achieve 90-99% statistical fidelity when the generative model is well-tuned and the real dataset is sufficiently large.

Tradeoffs and limitations

• ●Rare events or long-tail patterns are harder to capture because they appear fewer times in the training data.
• ●Synthetic data may introduce artifacts that are invisible to humans but can affect certain models.
• ●You still need a real dataset to train the generative model, so privacy risks are reduced but not eliminated.
• ●Legal teams must review synthetic data to ensure it does not accidentally reconstruct sensitive attributes.
• ●Quality depends heavily on the choice of model architecture and the amount of real data available.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. These agents can navigate complex healthcare or finance systems, perform tasks, and produce rich trajectories that reflect how users actually work. Coasty can turn these interaction logs into custom synthetic datasets and trajectories tailored to your domain and use case. Because the service is custom and contact-led, you work directly with the Coasty team to define your requirements, data scope, and use cases. There is no public price list or fixed package, you discuss your needs and receive a proposal tailored to your organization.